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Core Data Analysis.: Summarization, Correlation, and Visualization. Paperback - 2019
by Mirkin, B
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- Paperback
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Details
- Title Core Data Analysis.
- Author Mirkin, B
- Binding Paperback
- Pages xv, 524 p.
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2019
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # 34326AB
- ISBN 9783030002701 / 3030002705
- Weight 1.65 lbs (0.75 kg)
- Dimensions 9.21 x 6.14 x 1.09 in (23.39 x 15.60 x 2.77 cm)
- Category Computers - Data Base Management
- Dewey Decimal Code 004.015
About Lange & Springer Antiquariat Germany
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Lange & Springer Antiquariat (est. 1816) is a long-established scientific antiquarian bookstore based in Berlin. We specialize in rare and scholarly books spanning the sciences, medicine, mathematics, engineering, and the humanities — from historically significant early works to modern academic literature. Our curated selection includes antiquarian treasures as well as contemporary academic titles for researchers, collectors, and institutions worldwide.
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From the publisher
From the rear cover
This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank.
Features:
- An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter.
- Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc.
- Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning.
New edition highlights:
- Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering
- Restructured to make the logics more straightforward and sections self-contained
Core Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners.
